Association of prenatal antibiotics with foetal size and cord blood leptin and adiponectin
Bibliographic record
Abstract
Summary Background Early postnatal antibiotic use has been shown to promote excess weight gain, but it is unclear whether intrauterine exposure to antibiotics is associated with foetal growth and adiposity. The objective of this study was to examine associations of antibiotic prescription in each trimester of pregnancy with foetal size and adipokine levels at birth. Methods In 2128 pregnant women from the pre‐birth Project Viva cohort, from electronic medical records, we estimated antibiotic prescribing by timing during pregnancy. Outcomes were sex‐specific birth weight‐for‐gestational‐age z ‐score (BW/GA‐ z ) and levels of umbilical cord leptin and adiponectin. We used linear regression models adjusted for maternal age, pre‐pregnancy body mass index, parity, race/ethnicity, education, smoking during pregnancy, household income and child sex and additionally adjusted cord blood leptin and adiponectin models for gestation length. Results Of the 2128 women in our sample, 643 (30.2%) were prescribed with oral antibiotics during pregnancy. Mean (standard deviation) BW/GA‐ z was 0.17 (0.97), cord blood leptin was 9.0 ng mL −1 (6.6) and cord blood adiponectin was 28.8 ng mL −1 (6.8). Overall, antibiotic prescription in pregnancy was associated with lower BW/GA‐ z [multivariable adjusted β −0.11; 95% confidence interval {CI} −0.20, −0.01]. In trimester‐specific analyses, only second trimester antibiotic prescription was associated with lower BW/GA‐ z ( β −0.23; 95% CI −0.37, −0.08). Overall, antibiotic prescription in pregnancy was not associated with cord blood leptin or adiponectin levels. However, in trimester‐specific analyses, third trimester antibiotic prescription was associated with higher cord blood leptin ( β 2.28 ng mL −1 ; 95% CI 0.38, 4.17). Conclusions Antibiotics in mid‐pregnancy were associated with lower birth weight for gestational age, whereas third trimester antibiotics were associated with higher cord blood leptin.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".